Reports of our death are an exaggeration Part 1
Let’s not make it easy for the machines to replace us.
Another multi-parter long-windedly asking: “Are we really going to be technologically redundant?”
Answer the first: “Only if we let ourselves.”
“The report of my death was an exaggeration.”
—Mark Twain
In 2017, then-CEO of Deutsche Bank John Cryan thought his employees’ days were numbered. Machines would do for them. Not just back office grunts: everyone. Even, presumably, Cryan himself.1
“Today,” he warned, “we have people doing work like robots. Tomorrow, we will have robots behaving like people”.
You can see where he was coming from: what with high-frequency trading algorithms, AI medical diagnosis, Alpha Go and self-driving cars: the machines were coming for us. And this was before GPT-3. Since then, things have only got worse: the machines have taken over our routine tasks; soon they will take the hard stuff, too.
Now.
As long as there has been the lever, wheel or plough, humans have used technology to do tedious, repetitive tasks and to lend power and speed to our frail earthly shells. Humans have done this because it is smart: machines follow instructions better than we do — that’s what it means to be an “automaton”. At things they are good at, machines are quicker, stronger, nimbler, cheaper and less error-prone than humans.
But that’s an important condition: as George Gilder put it:
“The claim of superhuman performance seems rather overwrought to me. Outperforming unaided human beings is what machines are supposed to do. That’s why we build them.”
The division of labour
Nowadays, we must distinguish between traditional, obedient, rule-following machines, and randomly-make-it-up large language models — unthinking, probabilistic, pattern-matching machines. LLMs are the novelty act of 2023, at the top of their hype cycle right now, like blockchain, was a year ago, and like DLT they will struggle to find an enduring use case.
Traditional machines make flawless decisions, as long as both question and answer are pre-configured. We meatsacks are better at handling ambiguity, conflict and novel situations, and dealing with situations where question or answer are not well-formed. We’re not flawless — that’s part of our charm — but wherever we find a conundrum we can at least have a bash. We don’t crash. We don’t hang until dialogue boxes close. That’s the boon and the bane of the meatware: you can’t always tell when a human makes a syntax error.
This division of labour is how we’ve always used technology: the human figures out which field to plough and when; the horse ploughs it.
While, over its history, technology may have prompted the odd short-term dislocation — the Industrial Revolution put a bunch of basket-weavers out of work — the long-term prognosis has been benign: technology has, for millennia, freed humans to do things we previously had no time to try.
Technology opens up the design space. It reveals adjacent possibilities, expands the intellectual ecosystem, domesticates what we know and opens up frontiers to what we don’t.
Frontiers are places where we need smart people to figure out new tools and new ways of operating. Machines can’t do it.
But technology also creates space and capacity to indulge ourselves. Parkinson’s law states:
“Work expands to fill the time allotted for its completion.”
Technology also frees us up to care about things we never used to care about. The microcomputer made generating, duplicating and distributing documents far, far easier. There’s that boon and bane again.
So, before concluding that this time the machines will put us out of work we must explain how. Why is this time different? What has changed?
FAANGs ahoy?
In 2018, in an internal presentation, the head of the UBS Evidence Lab — a “sell-side team of experts that work across 55+ specialized areas creating insight-ready datasets” — remarked that the incipient competition for banks was not “challenger” banks like Revolute or Monzo, but big tech: the likes of Apple, Amazon, Facebook and Google.
The argument was this: banking comes down to three components: technology, reputation, and regulation.
For traditional banks, two of these — technology and reputation — present a substantial problem. The other — regulation — is formalistic, especially if you have a decent technology stack.
So, how do the banks stack up against the FAANGS?
Technology
Banks: Generally legacy, dated, patched together, under-powered, under-funded, conflicting, liable to fall over, susceptible to hacking.
FAANGS: Awesome: state of the art, natively functional, at cutting edge, well-funded, well-understood, robust, resilient. Ok could be hacked
Winner: C’mon: are you kidding? FAANGS all the way!
Reputation
Banks: Everyone hates the Financial Services industry.
FAANGS: Who doesn’t love Apple? Who wouldn’t love to have an account at the iBank? Imagine if banking worked like Google Maps!
Winner: FAANGS. Are banks even on the park?
Regulation
Banks: All over it. Capitalised, have access to reserve banks, connected, exchange memberships, etc.
FAANGS: OK there is a bit of investment required here — and regulatory capital is a thing — but nothing is insurmountable with the Amazon Flywheel no?
Winner: Right now, Banks have the edge. But look out banking types: the techbros are coming for you.
That the FAANGS will wipe the floor with any bank when it comes to technology is taken res ipsa loquitur — it needs little supporting evidence, just based on how lousy bank tech is — and, sure, the FAANGs have better standing with the public. Who doesn’t love Apple? Who does love Wells Fargo?2
Therefore, the argument goes, the only place where banking presently has an edge is in regulatory licences and approvals, capital, and regulatory compliance. It’s wildly complex and fiendishly detailed, the rules differ between jurisdictions and the perimeter between one jurisdiction and the next is not always obvious. To paraphrase Douglas Adams: “You might think GDPR is complicated, but that’s just peanuts compared to MiFID.”
But, but, but — any number of artificially intelligent startups can manage that regulatory risk, right?
But really. Let’s park a few uncomfortable facts and give Evidence Labs the benefit of the doubt:
So where are they?
Firstly — if banking is such a sitting duck for predator FAANGS, where the hell are they? It is 2023, for crying out loud. Wells Fargo is still with us. None of Apple, Amazon, or Google as so much as cast a wanton glance in Wells Fargo’s direction, let alone the Vampire Squid’s. Something is keeping the techbros away.
Techbros aren’t natural at banking
And it’s not just fear of regulation, capital and compliance: if it were, you would expect tech firms to be awesome at unregulated financial services.
But — secondly — they’re not.
We’ve been treated to a ten-year, live-fire experiment with how good tech firms will be in unregulated financial services — crypto — from which the banks — “trad fi” if you please — and, notably, the FAANGS have mainly stayed away.
It hasn’t gone well.
Credulous cryptobros have found, and promptly fallen down, pretty much every open manhole already known to the dullest money manager — and discovered some new ones of their own to fall down too. Helpfully, Molly White has kept a running score. Crypto, despite its awesome tech and fabulous branding, has been a disaster.
Tech brand-love-ins won’t survive first contact with banking
Thirdly — a cool gadget maker that pivots to banking and does it well has as much chance of maintaining millennial brand loyalty as does a toy factory that moves into dentistry.
That Occupy Wall Street gang? Apple fanboys, the lot of them. At the moment. But it isn’t the way trad fi banks go about banking that tarnishes your brand. It’s banking. No-one likes money-lenders. It is a dull, painful, risky business. Part of the game is doing shitty things to customers when they lose your money. Repossessing the Tesla. Foreclosing on the condo. That isn’t part of the game of selling MP3 players.
The business of banking will trash the brand.
Bank regulation is hard
Fourthly — regulatory compliance is not formalistic, and it is not “the easy bit of banking”. If you could solve it with tech, the banks would have long since done it. They have certainly tried. (Modern banks, by the way, absolutely are technology businesses, in a way that WeWork and Twitter are not).
Regulations change, contradict, don’t make sense, overlap, are fiddly, illogical, often counterproductive and subject to interpretation by regulators who themselves are fiddly, illogical and not known for their constructive approach to rule enforcement.
Getting regulations wrong can have bad consequences. Even apparently formalistic things like KYC and client asset protection. Banks already throw armies of bodies and legaltech at this and still, they are routinely breaching minimum standards and being fined millions of dollars.
The gorillas in the room
A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.
—Robert Heinlein
But in any case, park all the above, for it is beside the point. It overlooks the same core banking competence that Mr. Cryan did: quality people and quality leadership.
We have fallen into some kind of high-modernist swoon, in which we hold up ourselves up against machines as if techne is a Platonic ideal to which we should aspire.
So we set our children modernist criteria, too, from the moment they set foot in the classroom. The education system selects individuals by reference to how well they obey rules and how reliably and quickly they can identify, analyse and resolve known, pre-categorised, “problems”. But these are historical problems with known answers. This is a finite game. This is exactly what machines are best at.
Why are we systematically educating our children to compete with machines at things machines are best at?
If we tell ourselves that “machine-like qualities” are the highest human aspiration, we will naturally find humans wanting. We make it easy for the robots to take our jobs. We set ourselves up to fail.
But human qualities are different — humans can improvise, imagine, project in space and time, judge, narratise, analogise, interpret and assess — they can conceptualise Platonic ideals — in a way that algorithms cannot and LLMs can’t do except by pattern matching.
And there is the impish inconstancy, unreliability and unpredictability of the human condition — these make humans different, not inferior, to algorithms. They make humans difficult to control and manage by algorithm.
This is not a bad thing.
It is the point. We are not meant to be making it easy for machines to manage and control us. By suppressing our human qualities, we make ourselves more legible, machine-readable, triage-able and categorisable by algorithm. The economies of scale and process efficiencies this yields accrue to the machines and their owners, not us.
Why surrender before kick-off like that?
Next time
Part 2:
What it is to be a machine
Body and mind as a metaphor for the division of labour
AI overreach and the misconception of “Bayesian priors”
Playlist
To celebrate the 40th anniversary of its release on 1 November 1983 — a point closer in time to the end of World War 2 than the present — Billy Idol’s matchless 1983 album Rebel Yell. If anyone in the 1980s captured the spirit of Elvis, it was Billy Idol. In Steve Stevens, Idol had a guitar player as quietly influential as Scotty Moore, and who could make ray-gun noises with his guitar, and the album’s title track has its own article on the JC for its correct, if metaphorical, application of the legal concept of a licence.
In a rather mean irony, most of his employees have lasted longer than Cryan, who was replaced “amid losses and lack of direction” in 2018
This rather confuses the product for its manufacturer. We might feel different about Apple if, rather than making neat space-aged knick-knacks it made a business of coldly foreclosing mortgages, and charging usurious rates on credit card balances. You don’t think it would? Have you seen the cut it takes from the App Store?
Sorry JC, I think you are wildly optimistic when you contend that "humans can improvise, imagine, project in space and time, judge, narratise, analogise, interpret and assess — they can conceptualise Platonic ideals — in a way that algorithms cannot and LLMs can’t do except by pattern matching." As humans are nothing more than pattern matching algorithms trained on a lifetime of experience. Human babies can't do the things you list, and some would argue that most 20 year olds can't either. The human advantages you list are learnt, and if we can learn them, so can the next generation of machines. It's a race between AI and climate change but sooner or later one of them is going to put us back in our place in the animal kingdom.